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Postprocesamiento

En modelos de detección de objetos como YOLO, el postprocesamiento es esencial para refinar las predicciones en bruto realizadas por la red. Como vimos en la Lección 2, estos modelos tienden a producir un gran número de predicciones.

Este comportamiento se debe a la arquitectura del modelo, donde:

  • Cada celda de la rejilla predice múltiples anchor boxes.
  • Las celdas vecinas pueden detectar el mismo objeto.

Como resultado, varias cajas solapadas pueden representar el mismo objeto. Si conservamos todas las cajas predichas, la salida contendría muchos duplicados.

Para solucionar esto, el postprocesamiento incluye:

  • Umbralización por confianza: Filtra las detecciones con baja confianza.
  • Non-Maximum Suppression (NMS): Elimina cajas solapadas redundantes, conservando solo la de mayor confianza.

Este paso garantiza resultados más limpios y precisos.

El objetivo de esta lección es aprender a implementar filtros de postprocesamiento y entender cómo pueden ayudarnos a mejorar nuestros resultados.

Añadir la ruta del módulo de ayuda

Primero, añadimos el módulo de ayuda:

addpath('help-module');

Cargar un modelo

A continuación, cargamos el modelo al que aplicaremos las técnicas de postprocesamiento:

modelPath = fullfile(pwd, 'runs', 'detect', 'train10', 'weights');
configFile = fullfile(pwd, 'datasets', 'fruits_3_4998', 'data.yaml');

yolov8Det = utils.loadModel(modelPath, configFile);

Ejercicio 1 - Filtro de confianza

Implementa un filtro de confianza que elimine detecciones con un nivel de confianza inferior al umbral.

function filteredDetections  = filterByConfidence(detections, threshold)
    % Elimina detecciones con confianza por debajo de un umbral.
    % Inputs:
    %   detections - Array de structs, cada uno representando un resultado de detección con campos:
    %                .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                .conf  (Nx1): puntuaciones de confianza
    %                .cls   (Nx1): etiquetas de clase
    %   threshold  - Valor escalar que especifica la puntuación mínima de confianza necesaria para conservar una detección
    %
    % Output:
    %   filteredDetections - Array de structs con las detecciones de baja confianza eliminadas.

end

Solución

function filteredDetections  = filterByConfidence(detections, threshold)
    % Elimina detecciones con confianza por debajo de un umbral.
    % Inputs:
    %   detections - Array de structs, cada uno representando un resultado de detección con campos:
    %                .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                .conf  (Nx1): puntuaciones de confianza
    %                .cls   (Nx1): etiquetas de clase
    %   threshold  - Valor escalar que especifica la puntuación mínima de confianza necesaria para conservar una detección
    %
    % Output:
    %   filteredDetections - Array de structs con las detecciones de baja confianza eliminadas.

    n = numel(detections);
    filteredDetections = [];
    for i = 1:n
        detection = detections(i);
        conf = detection.conf;
        keep = conf >= threshold;
        if keep
           filteredDetections = [filteredDetections, detection];
        else
            fprintf('Filtered: removed low-confidence detection\n');
        end
    end
end

En el siguiente código, definimos una función anónima para el filtro que implementamos en el ejercicio anterior, que después usamos para crear un componente de postprocesamiento.

ScoreThreshold = 0.73;

removeLowScore = @(detections) filterByConfidence(detections, ScoreThreshold);
postprocessing = utils.PostProcessing(yolov8Det, {removeLowScore});

Este componente de postprocesamiento se utiliza en el siguiente código para aplicar nuestro filtro a las detecciones, que podemos controlar usando la variable ScoreThreshold.

utils.displayFilteredDetections(yolov8Det, 'pear.jpg', postprocessing);

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Ejercicio 2 - Filtro de confianza por clases

Como en el ejercicio anterior, debes implementar un filtro de confianza que elimine detecciones con una puntuación de confianza inferior a un umbral. Sin embargo, esta vez, cada clase debe tener su propio umbral personalizado.

function filteredDetection = filterByClassConfidence(detection, thresholds)
   % Filtra detecciones usando un umbral de confianza específico para cada clase.
    % Inputs:
    %   detections  - Array de structs, cada uno representando resultados de detección con campos:
    %                 .boxes (Nx4): bounding boxes
    %                 .conf  (Nx1): puntuaciones de confianza
    %                 .cls   (Nx1): etiquetas de clase (como enteros empezando en 1)
    %
    %   thresholds  - Vector de umbrales de confianza específicos por clase.
    %                 thresholds(i) es la confianza mínima requerida para la clase i.
    %
    % Output:
    %   filteredDetections - Array de structs con las detecciones de baja confianza eliminadas,
    %                        donde el filtrado se aplica por clase usando los umbrales correspondientes.

end 

function filteredDetections = filterByClassConfidence(detections, thresholds)
    % Filtra detecciones usando un umbral de confianza específico para cada clase.
    % Inputs:
    %   detections  - Array de structs, cada uno representando resultados de detección con campos:
    %                 .boxes (Nx4): bounding boxes
    %                 .conf  (Nx1): puntuaciones de confianza
    %                 .cls   (Nx1): etiquetas de clase (como enteros empezando en 1)
    %
    %   thresholds  - Vector de umbrales de confianza específicos por clase.
    %                 thresholds(i) es la confianza mínima requerida para la clase i.
    %
    % Output:
    %   filteredDetections - Array de structs con las detecciones de baja confianza eliminadas,
    %                        donde el filtrado se aplica por clase usando los umbrales correspondientes.

    n = numel(detections);
    filteredDetections = [];  % preasignar array de structs
    for i = 1:n
        detection = detections(i);

        cls = detection.cls;
        conf = detection.conf;
        keep = conf >= thresholds(cls);

        if keep
            filteredDetections = [filteredDetections, detection];
        else
            fprintf('Filtered: removed low-confidence detections (per class)\n');
        end
    end
end 

% Vector de umbrales por clase (índice = clase)
appleThreshold = 0.66;
orangeThreshold = 0.45;
pearThreshold = 0.61;

scoreThresholds = [appleThreshold, orangeThreshold, pearThreshold];
removeLowScorePerClass = @(detections) filterByClassConfidence(detections, scoreThresholds)

removeLowScorePerClass = function_handle with value:
    @(detections)filterByClassConfidence(detections,scoreThresholds)

postprocessing = utils.PostProcessing(yolov8Det, {removeLowScorePerClass});

utils.displayFilteredDetections(yolov8Det, 'multiple.jpg', postprocessing);

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figure_1.png

Intersection over Union (IoU)

En detección de objetos, un concepto fundamental es Intersection over Union (IoU). Esta métrica cuantifica el solapamiento entre dos bounding boxes: la caja predicha por el modelo y la caja ground truth. IoU se usa para evaluar qué tan precisa es una predicción en relación con la anotación real.

Formalmente, IoU se define como la razón entre el área de intersección y el área de unión de las dos cajas:

image_0.png

El valor de IoU está acotado entre 0 y 1:

  • IoU = 0 significa que no hay solapamiento.
  • IoU = 1 indica una coincidencia perfecta entre la predicción y el ground truth.

Ejercicio 3 - Calcular IoU

Implementa una función que calcule la Intersection over Union de dos bounding boxes:

Pista

$$ \textrm{unión}={\textrm{área}}_1 +{\textrm{área}}_2 -\textrm{intersección} $$ 

function iou = calculateIoU(b1, b2)
    % Calcula la Intersection over Union (IoU) entre dos bounding boxes.
    %
    % Inputs:
    %   b1 - Vector 1x4 que representa la primera bounding box [x1 y1 x2 y2]
    %   b2 - Vector 1x4 que representa la segunda bounding box [x1 y1 x2 y2]
    %
    % Output:
    %   iou - Valor escalar entre 0 y 1 que representa la IoU.
    %         IoU = 0 si no hay solapamiento.
    %         IoU = 1 si las cajas son idénticas.

    iou = calculateIoUSolution(b1, b2);

end

function iou = calculateIoU(b1, b2)
    % Calcula la Intersection over Union (IoU) entre dos bounding boxes.
    %
    % Inputs:
    %   b1 - Vector 1x4 que representa la primera bounding box [x1 y1 x2 y2]
    %   b2 - Vector 1x4 que representa la segunda bounding box [x1 y1 x2 y2]
    %
    % Output:
    %   iou - Valor escalar entre 0 y 1 que representa la IoU.
    %         IoU = 0 si no hay solapamiento.
    %         IoU = 1 si las cajas son idénticas.

    x1 = max(b1(1), b2(1));
    y1 = max(b1(2), b2(2));
    x2 = min(b1(3), b2(3));
    y2 = min(b1(4), b2(4));
    inter = max(0, x2-x1) * max(0, y2-y1);
    area1 = (b1(3)-b1(1))*(b1(4)-b1(2));
    area2 = (b2(3)-b2(1))*(b2(4)-b2(2));
    union = area1 + area2 - inter;
    if union>0
        iou = inter/union;
    else
        iou = 0;
    end

end

Test

utils.displayIoU([0 0 2 2], [2 2 4 4], @calculateIoU) % esperado 0

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utils.displayIoU([2 2 4 4], [2 2 4 4], @calculateIoU) % esperado 1

figure_3.png

utils.displayIoU([0 0 2 2], [1 1 3 3], @calculateIoU) % esperado 0.1429

figure_4.png

utils.displayIoU([0 0 2 2], [1 1 2 2], @calculateIoU) % esperado 0.25

figure_5.png

utils.displayIoU([0 0 3 3], [0.5 0 3 3], @calculateIoU); % esperado 0.8333

figure_6.png

Ejercicio 4 - Non-Maximum Suppression

En este ejercicio, tu objetivo es implementar Non-Maximum Suppression (NMS) para eliminar detecciones solapadas redundantes de la misma clase.

Cómo funciona NMS

El algoritmo básico de NMS consta de estos pasos:

  1. Filtrar por puntuación de confianza: Eliminar todas las cajas con confianza (objectness × probabilidad de clase) por debajo de un umbral (por ejemplo, 0.25).
  2. Ordenar cajas por puntuación: Ordenar las cajas restantes en orden descendente según sus puntuaciones de confianza.

  3. Suprimir cajas solapadas:

  4. Tomar la caja con mayor confianza, llamarla B1.

  5. Calcular la IoU (Intersection over Union) de B1 con todas las demás cajas.
  6. Si IoU > IoUThreshold (por ejemplo, 0.5), descartar esas cajas, ya que probablemente representan el mismo objeto.
  7. Repetir con la siguiente caja restante.

Pista

Usa la siguiente función para convertir bounding boxes al formato requerido por la función calculateIoU:

function boxXYXY = xywh2xyxy(boxXYWH)
    % Convertir [x y w h] a [x1 y1 x2 y2]
    boxXYXY = [boxXYWH(1), ...
               boxXYWH(2), ...
               boxXYWH(1) + boxXYWH(3), ...
               boxXYWH(2) + boxXYWH(4)];
end

function filteredDetections = filterByOverlapping(detections, iouThreshold, calculateIoU)
    % Elimina detecciones solapadas basándose en Intersection over Union (IoU).
    % Inputs:
    %   detections    - Array de structs que contiene resultados de detección con campos:
    %                  .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                  .conf  (Nx1): puntuaciones de confianza
    %                  .cls   (Nx1): etiquetas de clase
    %   iouThreshold - Umbral escalar de IoU. Las detecciones con IoU por encima de este 
    %                  valor (y de la misma clase) se consideran duplicadas.
    %   calculateIoU  - Function handle para calcular IoU entre dos cajas.
    %
    % Output:
    %   filteredDetection - Struct de detección con las cajas solapadas eliminadas,
    %                       conservando la de mayor confianza.
end

function filteredDetections = filterByOverlapping(detections, iouThreshold, calculateIoU)
    % Elimina detecciones solapadas basándose en Intersection over Union (IoU).
    % Inputs:
    %   detections    - Array de structs que contiene resultados de detección con campos:
    %                  .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                  .conf  (Nx1): puntuaciones de confianza
    %                  .cls   (Nx1): etiquetas de clase
    %   iouThreshold - Umbral escalar de IoU. Las detecciones con IoU por encima de este 
    %                  valor (y de la misma clase) se consideran duplicadas.
    %   calculateIoU  - Function handle para calcular IoU entre dos cajas.
    %
    % Output:
    %   filteredDetection - Struct de detección con las cajas solapadas eliminadas,
    %                       conservando la de mayor confianza.


    % Extraer componentes individuales de detección
    boxes = vertcat(detections.boxes);  % Nx4
    conf  = vertcat(detections.conf);   % Nx1
    cls   = vertcat(detections.cls);    % Nx1

    N = size(boxes, 1);         % Número total de detecciones

    % Ordenar detecciones por confianza descendente
    [~, order] = sort(conf, 'descend');

    keep = false(N,1);          % Máscara booleana para registrar qué cajas se conservan

    % Iterar sobre las detecciones en orden
    for k = 1:N
        idx = order(k);         % Índice de la detección actual
        keepThis = true;        % Indicador de si se conserva la caja actual

        % Comparar con cajas previamente conservadas
        for j = find(keep)'     % Índices de cajas ya conservadas
            if cls(idx) == cls(j)   % Comparar solo cajas de la misma clase
                iou = calculateIoU(xywh2xyxy(boxes(idx,:)), xywh2xyxy(boxes(j,:)));
                if iou > iouThreshold
                    keepThis = false;
                    break;
                end
            end
        end

        if keepThis
            keep(idx) = true;
        end
    end

    % Conservar solo las detecciones seleccionadas
    filteredDetections.boxes = boxes(keep,:);
    filteredDetections.conf  = conf(keep);
    filteredDetections.cls   = cls(keep);

    removed = sum(~keep);
    if removed > 0
        fprintf('Filtered: removed %d overlapping detections\n', removed);
    end
end

ScoreThreshold = 0.5;
removeLowScore = @(detections) filterByConfidence(detections, ScoreThreshold);

iouThreshold = 0.5;
removeOverlapping = @(detections) filterByOverlapping(detections, iouThreshold, @calculateIoU);

filters = {removeLowScore, removeOverlapping};
postprocessing = utils.PostProcessing(yolov8Det, filters);
utils.displayFilteredDetections(yolov8Det, 'pear.jpg', postprocessing);

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figure_7.png

utils.displayFilteredDetections(yolov8Det, 'orange.jpg', postprocessing);

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figure_8.png

utils.displayFilteredDetections(yolov8Det, 'orange2.jpg', postprocessing);

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figure_9.png

utils.displayFilteredDetections(yolov8Det, 'apple.jpg', postprocessing);

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figure_10.png

utils.displayFilteredDetections(yolov8Det, 'apple2.jpg', postprocessing);

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figure_11.png

utils.displayFilteredDetections(yolov8Det, 'multiple.jpg', postprocessing);

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Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed 36 overlapping detections

figure_12.png

Ahora, después de filtrar las predicciones, obtenemos buenos resultados; sin embargo, no tenemos idea de qué tan bueno es realmente el modelo.

En la siguiente lección, abordaremos esta cuestión.